Connectivity-Energy Coupling in the human brain: Mapping Glucose Demands of Structural Connectivity
Poster No:
2598
Submission Type:
Abstract Submission
Authors:
Yifan Mayr1,2, Aldana Lizarraga1, Arianna Sala3,1, Igor Yakushev1,2
Institutions:
1Technical University of Munich, Department of Nuclear Medicine, Munich, Germany, 2Graduate School of Systemic Neurosciences GSN-LMU, Munich, Germany, 3Université De Liège, Liege, Belgium
First Author:
Yifan Mayr
Technical University of Munich, Department of Nuclear Medicine|Graduate School of Systemic Neurosciences GSN-LMU
Munich, Germany|Munich, Germany
Technical University of Munich, Department of Nuclear Medicine|Graduate School of Systemic Neurosciences GSN-LMU
Munich, Germany|Munich, Germany
Co-Author(s):
Arianna Sala
Université De Liège|Technical University of Munich, Department of Nuclear Medicine
Liege, Belgium|Munich, Germany
Université De Liège|Technical University of Munich, Department of Nuclear Medicine
Liege, Belgium|Munich, Germany
Igor Yakushev
Technical University of Munich, Department of Nuclear Medicine|Graduate School of Systemic Neurosciences GSN-LMU
Munich, Germany|Munich, Germany
Technical University of Munich, Department of Nuclear Medicine|Graduate School of Systemic Neurosciences GSN-LMU
Munich, Germany|Munich, Germany
Introduction:
Operation of any communication system requires energy input. Recently, energy demands of the brain were shown to be closely linked to its structural connectivity (SC). The goal of the present study was to map region-wise glucose demands of SC.
Methods:
We analyzed diffusion tensor imaging (DWI), and fluorodeoxyglucose (FDG) positron emission tomography (PET) data of 55 healthy, middle-aged individuals. Each subject underwent 2 identical scanning sessions, separated by 8 weeks . Images were spatially parcellated into 106 brain regions. SC was measured by probabilistic tractography. From each scanning session, we obtained 55x106=5830 observations of relative FDG uptake. For each FDG uptake observation, 106 connection weights were available. To understand how each connection individually contributes to a region's energy consumption, we fitted a multilinear regression model between structural connection weights and relative FDG uptake. The complete dataset was split into a training (80%) and a test (20%) set, stratified by brain region. Significance level was set at 0.05 with Bonferroni correction; confidence interval was estimated from 10,000 bootstraps.
Results:
The model consistently explained nearly half of the variance in regional FDG uptake (Session 1: training R2 = 0.49, test R2 = 0.48; Session 2: training R2 = 0.51, test R2 = 0.49). Among the 106 model coefficients, 40 were statistically significant in session 1, and 36 in session 2 (Figure 1). Of those, 32 were present in both sessions and were highly consistent (Figure 2, Pearson r=0.99, p<0.001). The top three most and least glucose-demanding connections were preserved in both sessions. The most glucose-demanding connections were to the right posterior cingulate, left posterior cingulate, and the right insula; the least glucose-demanding connections were to the left superior temporal pole, left hippocampus, and the left mid-temporal pole (Figure 1).
Conclusions:
In the healthy human brain, there is a specific relationship between communication infrastructure and energy demands. Neural fibers contribute differently to a brain region's glucose metabolism, and this coupling pattern is reproducible. This normal map builds a reference for studying connectivity-energy coupling, or likely decoupling, in the diseased brain.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Diffusion MRI Modeling and Analysis
PET Modeling and Analysis
Physiology, Metabolism and Neurotransmission :
Cerebral Metabolism and Hemodynamics 1
Keywords:
CHEMOARCHITECTURE
Data analysis
Multivariate
NORMAL HUMAN
Positron Emission Tomography (PET)
Tractography
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review